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TRUVACE RECORD VERSION
record: TRV-2026-1171
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-22T06:54:05.808312Z
status: published
lens: p_space
sector: lifestyle
headline: Artificial intelligence in food and nutrition science: a paradigm-centric review of computational frameworks and system-level integration
dek: Precision nutrition on a global scale necessitates an understanding of food not as static collections of so-called macronutrients but rather as dynamic and heterogeneous biochemical matrices. These complex non-linear interactions between food composition, gastrointestinal digestion and the human microbiome are difficult to capture using traditional empirical experimental methods. This review articulates a paradigm-based framework that reconceptualizes artificial intelligence (AI) in food and function science, fr…
gain_title: (none)
problem_title: Finally, we demonstrate how Microbial Community-scale Metabolic Modeling (MCMM) and multimodal machine learning mechanistically couple specific dietary inputs with unique microbiome responses, yielding up to 95% diagnostic accuracy in differentiating diet-responsive metabolic states and individualized postprandial glycemic outcomes.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Finally, we demonstrate how Microbial Community-scale Metabolic Modeling (MCMM) and multimodal machine learning mechanistically couple specific dietary inputs with unique microbiome responses, yielding up to 95% diagnostic accuracy in differentiating diet-responsive metabolic states and individualized postprandial glycemic outcomes.
problem_evidence: (none)
quick_read: Precision nutrition on a global scale necessitates an understanding of food not as static collections of so-called macronutrients but rather as dynamic and heterogeneous biochemical matrices. These complex non-linear interactions between food composition, gastrointestinal digestion and the human microbiome are difficult to capture using traditional empirical experimental methods.

This review articulates a paradigm-based framework that reconceptualizes artificial intelligence (AI) in food and function science, from generic industrial applications to the computational modeling of physiological and biochemical phenomena. We specifically explore critical integrations of Physics-Informed Neural Networks (PINNs) with established data-driven methods to circumnavigate the epistemological limitations of entirely data-driven models within standardized frameworks ( e.g. , INFOGEST), assessing their use in simulating gastrointestinal mass transfer and dissolution kinetics to achieve predictive accuracies up to R 2 = 0.91 in complex protein digestibility matrices.
limitation: 
tag: Evidence-backed problem
key_points: Precision nutrition on a global scale necessitates an understanding of food not as static collections of so-called macronutrients but rather as dynamic and heterogeneous biochemical matrices. | These complex non-linear interactions between food composition, gastrointestinal digestion and the human microbiome are difficult to capture using traditional empirical experimental methods. | This review articulates a paradigm-based framework that reconceptualizes artificial intelligence (AI) in food and function science, from generic industrial applications to the computational modeling of physiological and biochemical phenomena.
rundown: Precision nutrition on a global scale necessitates an understanding of food not as static collections of so-called macronutrients but rather as dynamic and heterogeneous biochemical matrices. These complex non-linear interactions between food composition, gastrointestinal digestion and the human microbiome are difficult to capture using traditional empirical experimental methods.

This review articulates a paradigm-based framework that reconceptualizes artificial intelligence (AI) in food and function science, from generic industrial applications to the computational modeling of physiological and biochemical phenomena. We specifically explore critical integrations of Physics-Informed Neural Networks (PINNs) with established data-driven methods to circumnavigate the epistemological limitations of entirely data-driven models within standardized frameworks ( e.g. , INFOGEST), assessing their use in simulating gastrointestinal mass transfer and dissolution kinetics to achieve predictive accuracies up to R 2 = 0.91 in complex protein digestibility matrices.
sources:
- peer_reviewed | Food & Function | https://doi.org/10.1039/d6fo02990f | 2026-09-21
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